Semantic attributes represent the characteristics or latent attributes of a target instance. Part of the feasibility of zero-shot learning depends on whether the middle-level semantic attributes have sufficient discrimination and expressiveness [1]}, due to the lack of semantic attribute annotations in industrial datasets, so we summarized the semantic attribute description of each defect based on the relevant literature[2]}, [3]}, [4]} and the description of some company employees. Take the cylinder liner data set CLSDD used in the following experiments as an example, as shown in Figure.REF , CLSDD has 6 categories, including 5 defect categories (cavitation, wear, crack, convexity, shrinkage). Intuitively, when there are defects on the surface of our image, generally speaking, we first notice its defect characteristics and want to understand the related causes, so the description of the defect is composed of the defect color, range, shape, and reason for the formation of the defect, and the related attributes shown in Table REF  are formulated.
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